MXPA06006844A - Multi-level railway operations optimization system and method. - Google Patents

Abstract

A multi-level system for management of a railway system (50) and its operational components in which the railway system (50) has a first level configured to optimize an operation within the first level that includes first level operational parameters which define operational characteristics and data of the first level, and a second level configured to optimize an operation within the second level that includes second level operational parameters which define the operational characteristic and data of the second level. The first level provides the second level with the first level operational parameters, and the second level provides the first level with the second level operational parameters, such that optimizing the operation within the first level and optimizing the operation within the second level are each a function of optimizing a system optimization parameter. The levels can include a railroad infrastructure level (100), a track network level (200), a train level (300), a consist level (400) and a locomotive level (500).

Description

SYSTEM AND METHOD OF OPTIMIZATION OF MULTI-LEVEL RAILROAD OPERATIONS CROSS REFERENCE TO RELATED REQUESTS Any FIELD OF THE INVENTION The invention relates to optimizing rail operations, and more particularly to a system and method for optimizing rail operations that utilize a multi-level, broad system aspect.

BACKGROUND OF THE INVENTION Railroads are complex systems, with each component being interdependent in other components within the system. Attempts have been made in the past to optimize the operation of a particular component or groups of railway system components, such as for the locomotive, for a particular operating characteristic such as fuel consumption, which is a major component of the cost of operation of a railway system. Some estimates indicate that fuel consumption is the second largest rail system operating cost, followed only by labor costs. For example, the Patent of E.U.A. No. 6,144,901 proposes to optimize the operation of a train for a number of operating parameters, which include fuel consumption. However, optimize the performance of a particular train, which is just one component of a much larger system; which includes, for example, the trail rail network, other trains, crews, rail yards, starting points, and destination points, may not generate a comprehensive optimization of the total system. Optimizing the performance of only one component of the system (even though it can be an important component such as a train) can actually result in increased system costs augmented, because this approach of the prior art does not consider the interrelationships and impacts on other components and on the efficiency of the total rail system. As an example, train optimization ignores potential efficiencies for a locomotive within the individual train, whose efficiencies may be available if the locomotives were free to optimize their own performance. A system and method of planning in the rail trail network system is described in the U.S. Patent. No. 5,794,172. Motion planners such as these mainly focus on the movement of trains through the network based on objective business functions (BOF) defined by the railroad company, and not necessarily on the basis of optimizing performance or a particular performance parameter such as fuel consumption. In addition, the motion planner does not extend the optimization down the train (much less the consistency or locomotives), nor the railroad service and maintenance operations that are planned for the service of trains or locomotives. Thus, in the prior art, there is no recognition that the optimization of operations for a railway system requires a multi-level approach, with the collection of key data at each level and communicating data with other levels in the system.

COMPENDIUM OF THE INVENTION One aspect of the present invention is the provision of a multi-level system for the management of a railway system and its operational components in which the railway system comprises a first level configured to optimize an operation within the first level that includes parameters first level operational that define operational characteristics and data of the first level, and a second level configured to optimize an operation within second level that includes operational parameters of second level that define the operational characteristic and data of the second level. The first level provides the first level operational parameters to the second level, and the second level provides second level operational parameters to the first level, for that the optimization of operation within the first level and the optimization of the operation within the second level are each a function of optimizing a system optimization parameter. Another aspect of the present invention includes the provision of a method for optimizing an operation of a railway system having first and second levels comprising communicating from the first level to the second level a first level operational parameter that defines an operational characteristic of the first level, communicate from the second level to the first level a second level operational parameter that defines an operational characteristic of the second level, optimize a system operation through a combination of the first level and the second level based on a system optimization parameters, optimize an operation within the first level based in part on the system optimization parameter, and optimizing an operation within the second level based on a second level optimization parameters and based in part on the system optimization parameter. Another aspect of the present invention is the provision of a method and system for optimizing multilevel rail operations for a complex railroad system that identifies key operational constraints and data at each level, and communicates these restrictions and data at levels adjacent and optimizes performance at each level based on the data and constraints of adjacent levels. The aspects of the present invention also include establishing and communicating updated plans and monitoring and communicating compliance with plans at multiple levels of the system. Aspects of the invention further include optimizing the performance at the railroad infrastructure level, the rail trail network level, the individual train level within the network, the consistency level within the train, and the level of the locomotive. individual within the consistency. The aspects of the invention also include optimizing the performance at the railroad infrastructure level to allow service based on condition, rather than based on locomotive programming, which includes both temporary (or short-term) service requirements such as refueling and filling of other consumable materials on board the locomotive, and long-term service requirements such as replacement and repair of critical locomotive operating components, such as traction motors and engines. The aspects of the invention include optimizing the performance of the various levels in view of the objective operational functions of the railway operating company, such as on-time deliveries, ownership utilization, minimum fuel usage, reduced emissions, optimized crew costs, residence time, maintenance time and costs, and reduced total system costs.

These aspects of the invention provide benefits such as fuel use variability from reduced day to day, fuel savings for each locomotive operating within the system, grace recovery of the system from alterations, elimination of mission failures outside of the system. fuel, improved fuel inventory control logistics and reduced crew autonomy in driving decisions.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a graphic illustration of the origin of multiple levels of rail operations optimization of this invention, with railroad infrastructure, railroad track trace network, train, locomotive consistency and individual locomotive levels being illustrated in their respective relationships with each other. Figure 2 is a graphic illustration of the infrastructure level of railways that illustrate the inputs and outputs to the infrastructure processor at this level. Figure 3 is a diagram illustrating details of optimized service operations at the infrastructure level. Figure 4 is a diagram illustrating details of refueling operations optimized at the infrastructure level. Figure 5 is an outline of the level of the railroad track network that illustrates its relationship with railroad infrastructure on this and the train below it. Figure 6 is a diagram illustrating details of the trace network level of railway tracks, with inputs and outputs of the processor at this level. Figure 7 is a schematic illustrating entrances to and exits of an existing motion glider at the train level. Figure 8 is a schematic of a revised rail track network processor having a network fuel manager processor for optimization of additional fuel usage parameters. Figure 9 is a pair of row line diagrams, with the first diagram being an initial movement plane made without consideration of operational optimization and the second diagram being a modified plane as optimized for reduced fuel consumption. Figure 10 is a train level scheme that uses its relationship with its related levels. Figure 11 is a diagram illustrating details of the train level processor inputs and outputs. Figure 12 is a scheme of the level of consistency that illustrates its relationship with its related levels. Figure 13 is a diagram illustrating details of the inputs and outputs of the consistency level processor. Figure 14 is a graph illustrating the use of fuel as a planned time function for various operation modes at the consistency level. Figure 15 is a locomotive level scheme that illustrates its relationships with the level of consistency. Figure 16 is a diagram illustrating details of the locomotive level processor inputs and outputs. Figure 17 is a graph illustrating the use of fuel as a function of planned operation time for various operation modes at the locomotive level. Figure 18 is a graph illustrating locomotive level fuel efficiency as a measure of fuel usage per unit of energy as a function of the amount of energy generated at the locomotive level for various modes of operation.

Figure 19 is a graph illustrating various electrical system losses as a function of DC link voltage at the locomotive level. Figure 20 is a graph illustrating energy consumption as a function of engine speed at the locomotive level. Figure 21 is a schematic of a power management subsystem of a split power locomotive that has an on-board energy regeneration and storage capacity as configured and operated for fuel optimization.

DETAILED DESCRIPTION OF THE PREFERRED MODALITY Referring to Figure 1, the origin of multiple levels of a railroad system 50 is illustrated. As shown, the system comprises from the highest level to the lowest level: a level of railroad infrastructure 100, a level of trail network 200, train level 300, consistency level 400, locomotive level 500. As described here below, each level has its own unique operating characteristics, constraints, key operating parameters and optimization logic. In addition, each level interacts in a unique way with related levels, with different data being exchanged at each interface between the levels so that the levels can cooperate to optimize the total rail system 50. The method for optimizing the rail system 50 is the same if one considers the level of locomotive 50 upwards, or the infrastructure system of railroad 100 downwards. To facilitate understanding, the next approach will be presented, a top-down perspective.

RAILWAY INFRASTRUCTURE LEVEL The optimization of the railroad system 50 at the railroad infrastructure level 100 in Figures 1-4 is illustrated. As indicated in Figure 1, the levels of multilevel rail operations systems 50 and the method include top-down, the level of railroad infrastructure 100, the level of the trail network 200, the level of train 300, consistency level 400 and locomotive level 500. The level of railroad infrastructure 100 includes the lower levels of the trail network 200, train 300, consistency 400 and level of locomotive 500. In addition, the level infrastructure 100 contains other features and internal functions that are not displayed, such as service facilities, service diversions, fuel tanks, road edge equipment, rail yards, train crew operations, destinations, cargo equipment ( frequently referred to as recoveries), download equipment (often referred to as items), and access to data that impacts the infrastructure, such as: Rover, weather conditions, rail conditions, objective business functions (including costs, such as penalties for delays and routing of damages, and warnings to deliver on time), natural disasters, and governmental regulatory requirements. These are features and functions that are contained in the infrastructure level of railroad tracks 100. Most railway infrastructure level 100 is of a permanent basis (or at least a longer term base). Infrastructure components such as the location of road edge equipment, fuel tanks and service facilities are not subject to change during the course of any given train trip.

However, the real-time availability of these components may vary depending on availability, time of day, and use by other systems. These infrastructure level characteristics of railroad tracks 100 act as opportunities or resources and obligations in the operation of railroad system 50 at the other levels. However, other aspects of the railway infrastructure level 100 are operable to serve other levels of the railway system 50 such as trail networks, trains, duties or locomotives, each of which can be used as a function of a multi-level optimization criterion. such as total fuel, refueling, emissions output, resource management, etc. Figure 2 provides a scheme for the optimization of the railroad infrastructure level 100. It illustrates the infrastructure level 100 and the infrastructure level processor 202 which interacts with the trail level 200 and the train level 300 to receive input data. of these levels, as well as from within the railroad infrastructure level 100, to generate commands to and / or provide data at the level from trail network 200 and train level 300, and optimize operation within the railway infrastructure level 100 As illustrated in Figure 3, the infrastructure processor 202 may be a computer, which includes memory 302, computer instructions 304 that includes an optimization algorithm, etc. The level of infrastructure 100 includes, for example, train and locomotive service such as in maintenance facilities and service diversions to optimize these service operations, infrastructure level 100 receives infrastructure data 206 such as installation location, capabilities of installation (both static characteristics such as the number of service compartments, as well as dynamic characteristics, such as the availability of compartments, service crews, inventory of reserve parts), installation costs (such as hourly rates, decrease in time), and the most recently noticed data such as weather conditions, natural disaster and objective business functions. The infrastructure level also receives trace network level 208 data, such as the current train system program for the planned arrival and departure of train track equipment in service facilities, the availability of substitute power (i.e., locomotives). of replacement) in the installation and scheduled service. In addition, the infrastructure level receives train-level data 210, such as the current capacity of trains in the systems, particularly those with health issues that may require service based on additional condition (such as scheduled base station), location current, speed and course of trains, and anticipated service requirements when the train arrives. The infrastructure processor 202 analyzes this input data and optimizes the operation of the railroad infrastructure level 100 by issuing work orders or other instructions to the service facilities to serve the particular trains, as indicated in the block. 226, which includes instructions to prepare the work to be done such as work programming compartments, work crews, tools, and order spare parts. The infrastructure level 100 also provides instructions that are used by the lower level systems. For example, trail commands 228 are issued to provide data for reviewing the train movement plane in view of a service plane, announcing the rail yard of the service plane such as for reconfiguring the train, and providing substitute power for a train. replacement locomotive. Train commands 230 are issued at train level 300 so that the particular trains to be served may have restricted operation or provide on-site service instructions that are a function of the service plane. As an example of the operations of the infrastructure level 100, Figure 4 shows an optimized replenishment of the infrastructure level 400. This is a particular case of optimized service at the infrastructure level 100. The infrastructure data entry 406 at the infrastructure level 400 for optimize the refueling are related to fuel parameters. These include locations of refueling sites (which includes large service facilities as well as fuel depots, and even detours in which fuel trucks are dispatched) and total fuel costs, which includes not only the direct price per gallon of fuel, but also valuation and reduction of crew time, transportation costs of inventory, taxes, requirements of general and environmental expenses. The 408 trace network level input data includes the costs of changing the train schedule in the total motion plane to accommodate refueling or reduced speeds if the fuel supply is not done, as well as the topography of the trail in front of the trains as it has a greater impact on the use of fuel. Input data from train level 410 includes location and current speed, fuel level and fuel speed data (which can be used to determine the range of travel locomotive) as well as consistency settings so that they can be considered alternative locomotive power generation modes. The train schedule as well as train weight, load and length are relevant to the anticipated fuel usage speed. The outputs for the optimal fuel replenishment infrastructure level 400 includes optimization of the fuel supply site both in terms of the fuel supply instructions for each particular train but also as anticipated over some period of time for fuel inventory purposes. Other outputs include command data 428 for trace network level 200 to review the movement plane, and train level commands 430 for fuel supply instructions at the installation site, which includes schedules, as well as operational limitations on the train such as the maximum speed of fuel use while the train is routed to the fuel location. The optimization of railroad infrastructure operation is not a static procedure, rather it is a dynamic procedure that is subject to revision at regular scheduled intervals (such as every 30 minutes) or while significant events occur and are reported at the infrastructure level 100 (such as faults in train brakes and service installation problems). The communication between infrastructure level 100 and with the other levels can be done on a real time basis or almost in real time to allow the flow of key information necessary to maintain the current service plans and distributed to other levels. Additionally, the information can be stored for subsequent analysis of trends or the identification or analysis of characteristics of a particular level, performance, interactions with other levels or the identification of particular equipment problems.

RAILWAY TRAIL NETWORK LEVEL Within the railway road infrastructure operational plans, the level of the rail trail network 200 is optimized as illustrated in Figures 5 and 6. The level of the rail trail network 200 includes not only the trail design, but also planes for movement of the various trains on the trail design. Figure 5 shows the interaction of trace network level 200 with the infrastructure level of railway tracks 100 on the same and the individual trains under it. As illustrated, the trace network level 200 receives input data from the infrastructure level 100 and the train level 300, as well as data (or feedback) from within the network level of rail tracks 200. As illustrated in FIG. Figure 6, the trace network processor 502 can be a computer, which includes memory 602, computer instructions 604 that includes an optimization algorithm, and so on. As shown in Figure 6, the infrastructure-level 506 acts include information regarding weather condition, rail yard, substitute power, facilities and service plans, origins and destinations. The trail network data 508 includes information regarding the existing train movement schedule, business object functions, and network obligations (such as limitations on the operation of certain trail sections). The 510 train level input data includes information regarding locomotive location and speed, current capacity (health), service required, operational limitations, consistency settings, train loading and length. Figure 6 also shows the output of trace network level 200 which includes data 526 sent to the infrastructure level, commands 530 to the trains and optimization instructions 528 for the trail network level 200. The data 526 sent to the infrastructure level 100 include road edge equipment requirements, rail yard demands, service installation needs, and anticipated origin and destination activities. The 530 train commands include programming for each train and routing operational limitations, and the 528 trail network optimization includes reviewing the train system schedule. As with infrastructure level 100, the rail trail 200 network programming (or motion plan) is reviewed at periodic intervals or while material events occur. The communication of the input and output of critical data and commands can be done on a real-time basis to maintain the current respective planes. An example of an existing motion glider is described in the U.S. Patent. No. 5,794,172. Such a system includes a prior art computer-aided supply (CAD) system that has a motion planner for energy supply systems to establish a detailed movement plane for each locomotive and communicate it to the locomotive. More particularly, such a motion glider plans the movement of trains over a trace network with a defined planning horizon such as 8 hours. The motion glider attempts to optimize a Business Objective Function of the railroad track trail network level (BOF) which is the sum of the BOFs for individual trains at the train levels of the rail trail level network level. The BOF for each train is related to the train terminus point. It can also be attached to any point in the individual train journey. In the prior art, each train has an individual BOF for each planning cycle in a planning territory. Additionally, each trail network system may have a separate number of planning territories. For example, a trail network system can have 7 planning territories. As such, a train that will cross N territories will have N BOF in any case in time. The BOF provides a means of comparing the quality of two planes of movement. In the course of calculating each plane of train movement every hour, the motion glider compares thousands of alternative planes. The trace network level problem is highly constrained by physical trail design, train trail operational constraints, train capabilities, and conflict requirements for resources. The time required to calculate a plane of movement in order to support the dynamic origin of railroad operations is a greater obligation. For this reason, it is assumed that train performance data, based on pre-calculated and stored data based on train consistency, trail conditions, and train schedule. The procedure used by the motion glider calculates the minimum operating time for a train schedule by simulating the non-opposite motion of the train on the trail, with stops and residence for work activities. This procedure captures the run time through each trace segment and alternates the trace segment in the train path. A planning pad, such as a percentage of operating time, is then added to the predicted train run time and the padded time is used to generate the plane of motion. One of such a prior art movement glider is illustrated in Figure 20, where the train (and thereby form the train level, level of consistency, level / locomotive engine) is at an optimum speed F-) along the 2002 speed / fuel consumption curve resulting in reduced fuel consumption in the lower 2004 part of the 2002 curve. Typical train speeds exceed the optimum train speed F ^ to reduce train speeds average usually results in reduced fuel consumption. Figures 7 and 8 illustrate details of one embodiment of the invention and its benefits for trail network level movement planning 200. Figure 7 illustrates an example of a 700 motion glider for analyzing operational parameters to optimize the plane of motion train to optimize fuel use. The motion glider 702 receives input from the train level 300. The modality of Figure 7 of the motion glider 702 receives and analyzes messages for the motion glider 702 from external sources 712 with respect to fuel refill points of the Objective Functions Business (BOF) 710 that includes a planning cushion as mentioned above. A communication link 706 for fuel optimizers 704 in trains at train levels 300 is provided in order to transmit the most recent motion plane to each of the trains at train level 300. In the prior art, the movement planner tried to minimize the delays of meetings and passes. In contrast, the system according to one embodiment of the present invention uses these delays as an opportunity for fuel optimization at various levels. Figure 8 illustrates a motion glider to analyze additional operational parameters beyond those illustrated in Figure 7 to optimize fuel optimization. The network fuel manager 802 provides the trace network level 200 with functionality to optimize fuel usage within the trace network level 200 based on the Business Objective Function (BOF) 810 of each of the trains in the network. 300 train level, 812 motor performance of the trains and locomotives comprising those trains, 804 congestion data and 808 fuel weight factors. The motion glider at the trace network level receives 708 input from the level optimizer train 704 and network fuel manager 802. For example, the train level 200 provides the motion glider 702 with the engine failure and horsepower reduction data 708. The motion glider 702 provides a movement plane 706 at train level 200 and congestion data up to 804 at the administrator network fuel 802. Train level 200 provides engine performance data 812 to the network fuel manager 802. The 712 motion glider, at the trace network level 200, uses the Business Objective Function (BOF) for each train, the planning cushion and fuel refueling points 806 and the engine failure and horsepower reduction data 708, to develop and modify the movement plane for a particular train at train level 200. As mentioned earlier, the modality of Figure 8 of the 702 motion glider incorporates an 802 network fuel manager module or fuel optimizer that monitors the other performance s for individual trains and provides inputs to the motion glider to incorporate fuel optimization information in the plane of movement. This model 802 determines the refueling publications based on the estimated fuel usage and also fuel costs. A weight factor of fuel cost represents the parametric balance of fuel costs (both direct and indirect) against compliance with programming. This balance is considered together with the anticipated congestion on the train route. Decreasing a train of fuel optimization of the train level can increase the congestion at the level of trace network when delaying other trains especially in highly trafficked areas. The network fuel manager module 802 is interposed to the motion glider 702 within the trace network level 200 to establish the planning pad (number of low-voltage times in the plane before appreciably affecting other train movements) for each train and modifies the movement plane 706 to allow for individual train planning pads, with longer planning pads and joints and shorter passes than typical to provide improved fuel optimization. Another improvement specifies a superior planning cushion for trains that are equipped with a 704 fuel optimizer and those schedules are not critical. This provides savings to local trains and trains that run on lightly traveled rails. This involves an interface to the motion glider 702 to establish the planning cushion for the train and a modification to the movement plane 706 to allow the planning cushion for individual trains to be established. Figure 9 illustrates a representative group of row line graphs for the planned movement (motion plane 706) of trains (ie trains A and B) moving in opposite directions on an individual trail, thereby requiring that the trains meet and pass by a 906 detour. The row line shows the train location as a function of travel times for trains, with line A illustrating train A's journey as it moves from its initial location 902 near the top of the box to its final location 904 near the bottom of the box, and the train B trip from its initial location 908 to the bottom of the box to its final location 910 at the top of the box. The "original plane" 900 as shown in the first row line of Figure 9 is generated solely for the purpose of minimizing the time required to present train movements. This row line shows that train A enters detour 906 represented by horizontal line segment 906 at time t-, to allow train B to pass. Train A stops and wastes time at detour 906 from you to t2. The B train, as shown by line 908-910, maintains a constant speed of 908 to 910. The upper curved line 909 and dotted line extension covered 911 represents the fastest movement that the A train is capable of performing. The "modified plane" 950 as shown in the row line to the right of Figure 9 was generated with consideration for fuel optimization. It requires the train A to travel faster (steeper inclination of line 918-912 from ^ to t4), to reach a second detour plus instant 912, although somehow the subsequent time t4, for example, t4 is after that t The modified plane also requires train B to decrease its travel speed in time t3 to pass to second deviation 912. The modified plane reduces the time-waster time of train A to t5-t4 from previous t2-t1 and reduces the B train speed starting at t3 to create the fuel optimization opportunity at train level 300 as reflected by the combination of the two particular trains, while maintaining the trace network level motion plane on or near its first level of performance. Entrances to trail network level 702 motion planner also includes locations of fuel tanks, fuel cost ($ / gallon per tank and time cost for fuel or so-called "cost penalty"), engine efficiency as represented by the shift inclination in the use of fuel over the change in power in horsepower (for example, tilt of "fuel use" or "HP"), fuel efficiency as represented by the tilt of change in the use of fuel on the change of speed or time, deceleration of energy for locomotives with low or no fuel, adhesion factors (snow, rain, sandboxes, cleaners, lubricants), fuel level for locomotives in trains, and protected range for train fuel. The railroad track trail network level functionality established by the motion glider 702 includes determining the consistency energy required as a function of speed under current or projected operating conditions, and determining fuel consumption as an energy function, type of locomotive, and network trace. The motion glider determinations 702 can be for locomotives, for consistency or train that would include the assigned load. The determination can be a function of the sensitivity of the fuel change over the 'energy change (? Fuel /? HP) and / or change the horsepower over the speed (? HP /? Speed). The motion glider 702 further determines the dynamic compensation of the fuel velocity (as provided above) for counting the thermal transients (tunnels, etc.), and adhesion limitations, such as stress or low speed traction degree, which they can deteriorate predictions of movement, for example, expected speed. The 702 motion glider can predict the current out-of-fuel range based on the operational assumption such as the energy continues at the current level or an assumption with respect to the future trace. Finally, the detection of parameters that can be significantly changed can be communicated to the motion glider 702, and as a result, an action such as a change in the plane of motion may be required. These actions can be automatic functions that are communicated continuously, periodically, that is to say, based on an exception such as for detection of transients or predicted out-of-fuel conditions. The benefits of this trace network level operation 200 include allowing the 702 motion glider to consider the use of fuel by optimizing the plane of motion without considering the details at the consistency level, predicting fuel velocity as an energy function and speed, and by integration, to determine the total expected fuel required for the plane of motion. Additionally, the motion glider 702 can predict the speed of the programming deterioration and make corrective adjustments to the movement plane if necessary. This may include delaying the supply of one-way trains or re-routing of trains in order to lighten the congestion in the main line. The trace network level 200 will also be able to divide the state of dynamic consistency fuel into refueling determination. fuel at the first opportunity, which includes the consideration of energy loss, such as when a locomotive within a consistency closes or is forced to operate at reduced energy. The trace network level 200 will also allow the determination (at the locomotive level or consistency level) of optimal updates to the plane of movement. These aggregate optimization data reduce the signal processing and monitoring required in the movement plane or computer aided distribution procedures. The motion plane output of the trace network level 200 specifies where and when to stop for fuel, amount of fuel to be loaded, lower and upper speed limits for the train, time / speed at destination, and time distributed for supply of gas.

TRAIN LEVEL Figures 10 and 11 illustrate the operation of train level and relationships between train level 300 and the other levels. The train processor 1002 can include a memory 1102 and computer instructions 1104 that influence an optimization algorithm, etc. While the train level 300 may comprise a long train with distributed consistencies, each consistency with several locomotives and with numerous automobiles between the consistencies, the train level 300 can be of any configuration that includes more complex or significantly simpler configurations. For example, the train can be formed by a single locomotive consistency or an individual consistency with multiple locomotives in the train head whose configurations simplify the levels, interactions and amount of reported data of train level 300 at consistency level 400 and at locomotive level 500. In the simplest case, an individual locomotive without any car can constitute a train. In this case, train level 300, consistency level 400 and locomotive level 500 are the same. In such a case, the train level processor, the consistency level processor and the locomotive level processor may be composed of one, two or three processors. For discussion purposes a more complex train configuration is assumed, then the input data at train level 300, as shown in Figures 10 and 11, includes infrastructure data 1006, rail trail network data 1008, train data 1010, including train feedback, and consistency level data 1012. Train level output includes data sent to infrastructure level 1026 and to trail network level 1028, optimization within train level 1030 and commands for consistency level 1032. Infrastructure level input data for railroad tracks 1006 include weather conditions, road edge equipment, service facilities, and origin / destination information. The trail network level data entry 1008 includes train system programming, network obligations and trace topography. 1010 train data input includes load, length, current braking and energy capacity, train health, and train operational obligations. The consistency data entry 1012 includes the number and locations of the consistencies within the train, the number of locomotives in the consistency and the capacity for power control distributed within the consistency. Train-level entries 300 from different sources at 400 locomotive consistency level include the following: head end and train end locations (EOT), anticipated incoming trail topography and road edge equipment, plane of motion, climate (wind, humidity, snow), and adhesion management (friction). Train level entries 300 of consistency level 400 is typically aggregation information obtained from locomotives and potentially from freight cars. These include current operating conditions, current equipment status, equipment capacity, fuel status, consumable state, consistency health, optimization information for the current plane, optimization information for plane optimization. The current operating conditions of the consistency may include the total traction effort present (TE), dynamic braking effort, air brake effort, total power, speed, and fuel consumption speed. This can be obtained by consolidating all consistency information at consistency level 400, which includes locomotives at locomotive level 500 within the consistency, and other equipment at consistency. The current equipment status includes the locomotive speeds, the position of the locomotives and load within the consistency. Unit speeds can be obtained from each consistency level 400 and each level of locomotive 500 which includes deterioration due to adhesion / environmental conditions. This can be obtained from consistency level 400 or directly from locomotive level 500. The position of the locomotives can be partly terminated by train line information, GPS position sensitivity, and air brake pressure that is perceived by time delay. The race can be completed by the traction effort (TE), braking effort (BE), speed profile and trail. Equipment capacity can include locomotive speeds in consistency including maximum tractive effort (TEmax), maximum braking effort (BEmax), horsepower (HP), dynamic brake HP, and adhesion capability. The fuel status, such as the current and projected amount of fuel in each locomotive, is calculated for each locomotive based on the current fuel level and fuel consumption projected for the operating plane. Consistency level 400 adds this information by locomotives and sends the total range and levels / fuel status possible at known fuel supply points. They can also send information where the article becomes critical. For example, a locomotive within the consistency can finish the fuel even the train can go to the next fueling station, if there is enough energy available in the consistency to reach that point. Similarly, the status of other consumables other than fuel, sand, friction modifiers, etc., is reported and added to consistency level 400. These are also calculated based on the current level and projected consumption based on weather, trail conditions, the current load and plane. The train level adds this information and sends the total range possibly levels / consumable status at known service points. You can also send the information where the article becomes critical. For example, if the limited adhesion operation requiring sand is not expected during the operation, it may not be critical that the sand equipment be served. The health of the consistency can be reported and may include failure information, degraded performance and maintenance requirements. The optimization information for the current plane can be reported. For example, this may include fuel optimization at consistency level 400 or locomotive level 500. For fuel optimization, as shown in Figure 14, the data and information for consistency level fuel optimization is represented by the tilt and shape of the line between the operational points 1408 and 1410. Further, the optimization information for the plane optimization may include the data and information as between the operating points 1408 and 1412, as shown in Figure 14 , for consistency level 400. Also as shown in Figure 11, the output data 1026 sent by train level 300 to infrastructure level 100 include information regarding the location, direction and speed of the train, the health of the train, decrease in operational speed of train performance in view of health conditions, service needs , short-term needs such as related to consumables as well as long-term needs such as equipment system repair requirements. The data 1028 sent from the train level 300 to the level of the trail network of railroad tracks 200 includes train location, speed heading, fuel levels, range and use, and train capabilities such as power, dynamic braking, and driving. friction. The optimization performance within train level 300 includes distributing energy to consistencies within the train level, distributing dynamic braking loads at consistency levels within the train level and pneumatic braking to cars within train level, and adhesion of wheels of consistencies and railroad cars. Output commands at consistency level 400 include motor speed and power generation, dynamic braking and wheel / rail adhesion for each consistency. Train level output commands 300 at consistency level 400 include energy for each consistency, dynamic braking, pneumatic braking for total consistency, total tensile stress (TE), trail adhesion management such as sand / lubricant application, flat motor cooling, and hybrid motor plane. An example of such a hybrid motor plane is shown in greater detail in Figure 21.

LEVEL OF CONSISTENCY Figures 12 and 13 illustrate the relationships of consistency level and data exchange with other levels. The consistency level processor 1202 includes a memory 1302 and processor instructions 1304 that include optimization algorithms, etc. As shown in Figure 12, the consistency level entries, as illustrated by consistency level 400 with optimization algorithms, include train level data 1210, locomotive level data 1214, and locomotive level 500 data 1212. consistency 400. The outputs include data 1230 at train level 300, commands 1234 at locomotive level 500, and optimization 1232 at consistency level 400.

As an input, the train level 300 provides data 1210 associated with train load, train length, current train capacity, operational obligations, and data of one or more consistencies within the train level 300. The information 1210 sent from the level from locomotive 500 to consistency level 400 may include current operating conditions and current equipment status. The current locomotive operating conditions include data that is passed to the consistency level to determine the total performance of the consistency. This can be used for feedback to the operator or the railway track control system. They can also be used for consistency optimization. These data can include: 1.- Traction effort (TE) (monitoring and dynamic braking) -this is calculated based on current / voltage, motor characteristics, gear portion, wheel diameter, etc. Alternatively, it can be calculated from plotting bar instrumentation or train dynamics that know the train and trace information. 2.- Horsepower (HP) -this is calculated based on the current / voltage alternator characteristics. It can also be calculated based on the current / voltage information of the traction motor or other means such as tensile stress and locomotive speed or engine speed and fuel flow rate. 3.- Establishment of throttle valve level. 4. - Air brake levels. 5.- Application of friction modifier, such as timing, type / quantity / location of friction modifiers, for example, sand and water. The current locomotive equipment status may include data, in addition to one of the above items from a to e, and for rail-level feedback and back-up at the level of the rail trail network. This includes: Equipment temperature such as motor, traction motor, inverter, dynamic braking grid, etc. A measure of the reserve capacity of the equipment at a particular point in time and can be used to determine when to transfer energy from one locomotive to another. Equipment capacity such as a measure of reserve capacity. This may include available horsepower (considering environmental conditions, engine capacity and cooling), available tensile stress / braking effort (considering trail / rail conditions, equipment operating parameters, equipment capacity), and friction handling capacity (both friction improvers and friction reducer).

Fuel level / fuel flow rate - the amount of fuel remaining can be used to determine when to transfer energy from one locomotive to another. The fuel tank capacity along with the amount of remaining fuel can be used by the train level and recovered to the level of rail trail network to decide the refueling strategy. This information can also be used for traction management (TE) imitated adhesion. For example, if there is a limited region of critical adhesion of the forward operation, the filling of the fuel tank can be planned to allow filling before entry of consistency into the region. The other optimization is to keep more fuel in locomotives that can convert that weight into useful traction effort. For example, a towing locomotive typically has a better rail and can more effectively convert weight to tensile stress provided to the shaft / motor / energy electronics that are not imitated (from the aforementioned equipment capacity level). The fuel flow rate can be used for total trip optimization. There are many types of fuel level sensors available. Fuel flow sensors are also currently available. However, it is possible to estimate the fuel flow velocity of previously known / perceived parameters on board the locomotive. In one example, the fuel injected per engine stroke (mm3 / stroke) can be multiplied by the number of strokes / seconds (rpm function) and the number of cylinders, to determine the fuel flow rate. This can also be compensated to return the fuel speed, which is a function of rpm engine, and environmental conditions. Another way to estimate the fuel flow rate is based on models that use HP traction, auxiliary HP and loss / efficiency estimates. The available fuel and / or flow velocity can be used for the total locomotive use balance (with appropriate weight if necessary). It can also be used to direct more efficient locomotive use of higher fuel in preference to less efficient locomotives (within the fuel availability obligation). Fuel rank / consumable-available fuel range (or any other consumable) is another piece of information. This is calculated based on the current fuel status and the projected fuel consumption based on the plane and available abortion fuel efficiency information. Alternatively, this can be inferred from models for each team or from past performance with correction for environmental conditions or based on the combination of these two factors. Friction modifier level - Information regarding the quantity and capacity of friction modifiers can be used to distribute strategy optimization (transfer from one locomotive to another). This information can also be used for the rail trail network and infrastructure levels to determine the refill strategy. Degradation / use of equipment - Cumulative locomotive usage information can be used to ensure that a locomotive is not overused. Examples of these may include the total energy produced by the engine, dynamic braking grid temperature profile, etc. This can also allow the locomotive operation that results in more use to some components if they are scheduled for revision / replacement in any way. Locomotive position-the position and / or direction of confrontation of the locomotive can be used for consideration of power distribution based on factors such as adhesion, train control, noise, and release. Locomotive health-health of the locomotive includes the current condition of the locomotive and its key subsystems. This information can be used for optimization of consistency level and through the network of trace and infrastructure levels for maintenance / programming services. Health includes component failure information for faults that do not degrade the current locomotive operation such as individual axle components in an AC electro-motion locomotive that does not reduce the horsepower speed of the locomotive, subsystem degradation information, such as hot environmental condition, and engine water not fully heated, maintenance information such as wheel diameter mismatch information and potential speed reductions as partially clogged filters. Operational parameter or condition relationship information - a relationship can be defined to one or more parameters or operating conditions. For example, Figure 17 is illustrative of the type of relationship information at the locomotive level that can be developed which illustrates and / or defines the relationship between fuel usage and time for a particular plane of motion as shown by the line 1402. This relationship information may be sent from locomotive level 500 to consistency level 400. This may include the following: Tilt 1704 in the current operating plane time (reduction of fuel consumption per unit time increasing for example in gallons / seconds). This parameter provides the amount of fuel reduction for each unit of travel time increase. The fuel increase between the fastest plane 1710 and the current plane 1706. This value corresponds to the difference in fuel consumption between points F3 and F ,, as shown in Figure 17. Fuel reduction between the optimum plane 1712 and the current plane 706. This value corresponds to the difference in fuel consumption between points F1 and F4 Figure 17. The reduction of fuel between the plane distributed the current plane. This value corresponds to the difference in fuel consumption between points F-i and F2 of Figure 17. The complete fuel as a function of time profile (which includes range). Any other consumable information.

For optimizations at consistency level 400, multiple estimates of closed turn can be made through the level of consistency and each of the locomotives or the locomotive level. Among the inputs of the consistency level from the front of the consistency level are operator inputs, anticipated demand inputs, and locomotive optimization and feedback information. The flow of information and sources of information between consistency level include: 6.- Operator inputs, 7.- Movement plane entries, 8.- Trace information, 9.- Sensor / model inputs, 10.- Inputs of the locomotives / cargo cars, 11.- Optimization of consistency, 12.- Commands and information for each of the locomotives in the consistency, 13.- Flow of train and movement optimization information, and 14.- State / general health and other information about the consistency and the locomotives in the consistency. Consistency level 400 uses information from / on each of the locomotives in consistency to optimize consistency level operations, to provide feedback at train level 300, and to provide instructions at locomotive level 500. This includes current operating conditions, potential fuel efficiency improvements possible for the current point of operation, potential operational changes based on the profile, health status of the locomotive. There are three categories of functions performed by consistency level 400 and the associated consistency level processor 1202 to optimize consistency performance. The optimization of internal consistency, consistency movement optimization, and consistency monitoring and control. Internal optimization functions / algorithms optimize consistency fuel consumption by controlling operations of various internal equipment to consistency such as locomotive throttle valve commands, brake commands, friction modifier commands, anticipation commands. This can be done based on current demand and taking into account future demand. Optimization of consistency level performance includes energy and distribution of dynamic braking between locomotives between consistency, as well as the application of friction improvement and reducers in points along the consistency for friction handling. The functions of optimization of movement of consistency and algorithms fasting to optimize the operation of the train and / or the operation of the plane of movement. The consistency control / monitoring functions help railroad track controllers with data regarding the current operation and state of the consistency and the locomotives / loads in the consistency, the state of the consumables, and other information to assist the railroad tracks with consistency / locomotive / trail maintenance.

Consistency level optimization 400 provides optimization of current consistency operations. For consistency optimization, in addition to the information listed above, you can also send other locomotive information. For example, to optimize fuel, the ratio between fuel / HP (fuel efficiency measurement) and horsepower (HP) as shown in Figure 18 by line 1802 can pass from each locomotive to the consistency level controller 1202 An example of this relationship is shown in Figure 18. Referring to Figure 18, the data may also include one or more of the following items: 1804 Fuel / HP tilt as a function of HP in the present horsepower operatives. This parameter provides a measure of fuel speed increase per horsepower increase. The maximum horsepower 1808 and the increase in fuel speed that corresponds to these horsepower. The most efficient operational point information 1812. This includes horsepower and fuel speed change to operate at this point. Complete fuel flow rate as a function of horsepower.

The update time and the amount of information can be determined based on the type of complexity of the optimization. For example, the update can be made based on significant change. This includes level change, larger speed change or equipment status changes that include operational mode failures or changes or significant fuel / HP changes, for example, a 5% variation. Ways to optimize include sending only tilt (item to top) at the current operating point can be done at a slow data rate, for example, once per second. Another way is to send items to, d, and c once and then send the updates only when there is a change. Another option is to send the article d only once and only update the points that change periodically such as once per second. The optimization within the consistency considers factors such as fuel efficiency, consumable availability and equipment / subsystem status. For example, if the current demand is for 50% horsepower for total consistency (the consistency of the prior art all have the locomotives in the same energy, here at 50% horsepower for each), it may be more Efficient operate some locomotives at less than 50% horsepower speed and other locomotives at more than 50% horsepower speed so that the total energy generated by the consistency equals operator demand. In this case, the most highly efficient locomotives will operate at higher horsepower than locomotives of lower efficiency. This distribution of horsepower can be obtained by several optimization techniques based on horsepower as a function of fuel velocity information obtained for each locomotive. For example, for smaller horsepower distribution changes, the inclination of the horsepower function can be used as a function of fuel speed. This distribution of horsepower can be modified to achieve other objective functions or to consider other obligations, such as train control / handicap forces based on other feedback from the locomotives. For example, if one of the locomotives is low on fuel, it may be necessary to reduce its load to conserve fuel if it is required that the locomotive produce a larger amount of energy (horsepower / hour) before refueling, even if This locomotive is the most efficient. Other input information for each locomotive at locomotive level 500 can be provided at consistency level 400. This other locomotive level information includes: Maintenance cost. This includes the cost of routine / scheduled maintenance due to wear and tear that depends on horsepower (ex. $ / Kwhr) or increased traction effort. Transistant capacity. This can be expressed in terms of the continuous operating capacity of the locomotive, the locomotive's maximum capacity and the constant and transient time gain. Efficiency of fuel in each point of operation. Inclination at each operation point. This parameter provides an amount of fuel speed increase per horsepower increase. The maximum horsepower at each point of operation and the increase in fuel speed that corresponds to these horsepower. The most efficient operational point information at each operation point. This includes horsepower and fuel speed change to operate at this point. Complete fuel flow rate versus horsepower curve at each operating point. Fuel range (and other consumables), based on the current fuel level and the projected plane and fuel consumption speed. If the full profile information is known, the total consistency optimization considers the total fuel and expended consumables. Other weight factors that can be considered include locomotive maintenance cost, transient capacity and issues such as train control, and limited adhesion operation. Additionally, if the consistency level fuel use form as a function of time as illustrated in Figure 14 changes significantly due to its transistor origin (e.g., the temperature of electrical equipment such as traction motors, alternators or storage elements), then this curve needs to be regenerated for several potential energy distributions for the current plane. Similar to the previous section, data can be sent periodically or once at the start and updates are sent only when there is a significant change. As input to the movement planes, optimization information can be developed at consistency level 400. Information can be sent from locomotive level 500 to be combined with the level of consistency with other information or aggregated with other level data. of locomotive to be used by the level of railroad network 200. For example, to optimize fuel, fuel consumption information as a function of plane time, for example, the time to reach the destination or an intermediate point such as gasket or pass, one can pass from each locomotive to consistency controller 1202. To illustrate one embodiment of the optimization operation at consistency level 400, Figure 14 illustrates the level of consistency as a function of fuel use versus time. A line denoted as 1402 represents use of fuel against time at the consistency level for a consistency programmed to go from point A to point B (not shown). Figure 14 shows the fuel consumption as a function of time as derived by the train. The tilt of line 1404 is the fuel consumption against time in the current plane. Point 1406 corresponds to the current operation, 1408 for maximum distributed time, 1410 corresponds to the best time it can do, and 1412 corresponds to the most efficient fuel operation. Under the current plane, it will consume a certain amount of fuel and will arrive there after a certain time has elapsed. It is also assumed that between points A and B, the train at the consistency level assumes to operate without considering other trains in the system. while it can reach its destination within the time currently distributed to it, for example, t2. The optimization is run autonomously on the train to reach point B. As noted above, the outputs of consistency level 400 include data for train level 300, commands and controls for locomotive level 500 as well as optimization of the Internal consistency level 400. Train level 1230 level outputs include data associated with consistency health, consistency service requirements, consistency energy, consistency braking effort, level of fuel, and fuel use consistency. In one embodiment, the consistency level sends the following types of additional information to be used at train level 300 for train level optimization. To optimize only the fuel, the fuel consumption information as a plane time function (time to reach the destination or an intermediate point such as gasket or pass) can be passed from each of the consistencies to the train / track controller. railway. Figure 14 describes an embodiment of the invention for fuel optimization and identifies the type of information and relationship between the fuel usage and the time that can be sent by the level of consistency at the train level. Referring to Figure 14, this includes one or more of the items listed below. The inclination 1404 in the current operating plane time (increase in time per unit of fuel consumption reduction: gallons / second). This parameter of the amount of fuel reduction for each unit of time increase. The fuel increase between the fastest plane and the current plane. This value corresponds to the difference in fuel consumption between points 1410 and 1406. Fuel reduction between the best plane and the current one. This value corresponds to the difference in fuel consumption between points 1406 and 1412 of Figure 14. The fuel reduction between the distributed plane and the current plane. This value corresponds to the difference in fuel consumption between points 1406 and 1408 of Figure 14. The complete fuel as a time profile function as illustrated in Figure 14 by line 1402. As noted in Figure 13 , consistency level 400 provides output commands at locomotive level 500 near the current engine speed and power generation and anticipated demands. Dynamic braking and horsepower requirements are also provided at the locomotive level. The signals / commands of the level of consistency at the locomotive or locomotive level within the consistency level include operational commands, adhesion modification commands, and anticipation controls. Operational commands can include throttle valve arrangements for each of the locomotives, dynamic braking effort / traction effort to be generated for each of the locomotives, train air brake levels (which can be expanded to brakes) of individual automobile air in the case that electronic air brakes are used and when individual cars / group of automobiles are selected), and independent air brake levels in each of the locomotives. The adhesion modification commands are sent to the level of locomotive or cars (for example, in the back of the locomotive) to distribute friction increase material (sand, water, or snow impulse) to improve the adhesion of that locomotive or towing locomotives or to be used for another consistency that uses the same trail. Similarly, distribution commands for fiction decrease material are also sent. The commands include, the type and amount of material to be distributed along with the location and duration of material distribution. The anticipation controls include actions to be taken by the individual locomotives within the locomotive level to optimize the total trip. This includes pre-cooling the motor and / or electrical equipment to improve the short-term speed or directly go through the higher environmental conditions. Even preheating can be performed (for example, water / oil may need to be at a certain temperature to fully charge the engine). Similar commands can be sent to the locomotive level and / or storage servers of a hybrid locomotive, as illustrated in Figure 21, to adjust the amount of energy storage before a direct demand cycle. The timing of the updates sent to and from the consistency level and the amount of information can be determined based on the type and complexity of the optimization. For example, the update may occur at a predetermined point in time, at regularly scheduled times or when significant changes occur. The latter may include: significant changes in the state of equipment (for example the failure of a locomotive) or operational mode changes such as operation degraded due to adhesion limits, or significant fuel, horsepower, or programming changes such as a change in horsepower by 5 percent. There can be many ways to optimize based on these parameters and functions. For example, only the inclination (item to previous) of the use of fuel as a function of time at the current operating point can be sent and this can be done at a slow speed, such as once every 5 minutes. Another way is to send items to, b, and c once and only send updates when there is a change. Even another option is to send only the article d once and only update points that change periodically, such as once every 5 minutes. As indicated in the previous discussion, with simplified versions of train configurations, such as consistencies of individual locomotive and / or individual locomotive trains, the relationship and extension of communication between train level 300, consistency level 400 and level of Locomotive 500 becomes less complex, and in some modes, it collapses into less than three separate operating levels or processors, possibly with all three levels operating within an individual operating level or processor.

LOCOMOTIVE LEVEL Figures 15 and 16 illustrate the relationship of the locomotive level 300 with the consistency level 400 and optimization of the internal locomotive operation through commands for several locomotive subsystems. The locomotive level includes a processor 1502 with optimization algorithms, which may be in the form of a memory 1602 and processing instructions 1604, etc. Input data at the locomotive level include consistency level 1512 data and locomotive level 1514 data (including locomotive feedback). The output of the locomotive level includes data 1532 at the level of consistency and optimization of performance data 1534 at the level of the locomotive. As shown in Figure 16, the 1512 input data of the consistency level includes a tensile stress command, locomotive engine speed and horsepower generation, dynamic braking, friction management parameters, and anticipated demands on the engine and propulsion system. Input data 1514 of the locomotive level includes locomotive health, measured horsepower, fuel level, fuel usage, measured traction effort and stored electrical power. The latter is applicable to modes using hybrid vehicle technology as shown and described above in relation to the hybrid vehicle of Figure 21. The data output 1532 at the consistency level includes locomotive health, friction handling, valve setting strangulation, and fuel use, level and scale. Locomotive optimization commands 1534 for locomotive sub-systems include engine speed for the engine, engine cooling for the engine cooling system, DC link voltage for the inverters, torque commands for the engines of traction, and loading and use of electric power from the electrical energy storage system of hybrid locomotives. Two other types of entries include operator entries and anticipated demand entries. The flow of information and information sources at locomotive level 500 includes: a. Operating inputs, b. Movement plane entries, c. Trace information d. Sensor / model inputs, e. On-board optimization, f. Information flow for optimization of consistency and movement, and g.- State / general health and other information for consolidation of consistency and for optimization / programming of railway tracks. Three categories of functions performed by the locomotive level include internal optimization functions / algorithms, functions / algorithms for locomotive movement optimization, and locomotive control / monitoring. Internal optimization functions / algorithms optimize locomotive fuel consumption by controlling operations of several internal locomotive equipment, for example, engine, alternator, and traction motor. This can be done based on current demand and taking into account future demand. The functions of locomotive movement optimization and / or algorithms help to optimize the operation of the consistency and / or operation of the movement plane. Locomotive control / monitoring functions help consistency and railroad track controllers with data regarding the operation and current state of the locomotive, the status of consumables and other information to assist railways with maintenance of locomotive and trace. Based on the obligations imposed at the locomotive level, the operating parameters that can be optimized include motor speed, DC link voltage, torque distribution and power source. For a given horsepower command, there is a specific engine speed that produces optimum fuel efficiency. There is a minimum speed below which the diesel engine can not withstand the demand for energy. At this engine speed the combustion of fuel does not happen in the most efficient way. While the engine speed increases the fuel efficiency improves. Nevertheless, the losses as friction and increase of resistance and therefore an optimum speed can be obtained where the total motor losses are the minimum. This fuel consumption versus engine speed is illustrated in Figure 20 where the 2002 curve is the locomotive's total performance range and the 2004 point is the optimum performance for fuel versus speed. The DC link voltage in an AC locomotive determines the DC link current for a given energy level. The voltage typically determines the magnetic losses in the alternator and the traction motors. Some of these losses are illustrated in Figure 19. The voltage also determines the losses of change in electronic energy devices and shock absorbers. It also determines the losses in the devices used to produce the alternator field excitation. On the other hand, the current determines the i2r losses in the alternator, traction motors and power cables. The current also determines the conduction losses in the energy semiconductor devices. The DC link voltage can be varied so that the sum of all losses is a minimum. As shown in Figure 19, for example, the alternator current losses against DC link voltage are plotted as a line 1902 the alternator magnetic core losses against DC link voltage are plotted as line 1906 and the current losses of motor versus DC link voltage are plotted as line 1904 which is substantially optimized on line 1908 at DC link voltage Vi. For a specific horsepower demand, the energy distribution (torsion distribution) for the six extraction axes of a locomotive mode can be optimized for fuel efficiency. Losses in each traction motor, even if it produces the same torque or same horsepower, can be different due to wheel slip, wheel diameter differences, operating temperature differences and differences in motor characteristics. Therefore, the distribution of energy between each of the axes can be used to minimize losses. Some of the axes can even be turned off to eliminate electrical losses in those traction motors and associated electronic power devices. In locomotives with additional energy sources, for example, hybrid locomotive as shown in Figure 21, the selection of the optimal energy source and the appropriate amount of energy drawn for each of the sources (so that the sum of the energy delivered is what the operator is demanding) determines the fuel efficiency. From there the locomotive operation can be controlled to obtain the best fuel efficient point of operation at any time. For consistencies or locomotives equipped with fiction management systems, the amount of friction seen by cargo cars (especially at higher speeds) can be reduced by applying friction reduction material on the rail behind the locomotive. This reduces fuel consumption since the tensile stress required to pull the load has been reduced. This amount and timing of distribution can also be used based on knowledge of the rail and load characteristics. A combination of two or more of the above variables (motor speed, DC link voltage and torque distribution) together with auxiliaries such as motor and equipment cooling can be optimized. For example, the maximum available DC link voltage is determined by the motor speed and from there it is possible to increase the motor speed beyond the optimum (based on motor consideration only) to obtain a higher voltage which results in a optimal operating point.

There are other considerations for optimization once the total operational profile is known. For example, parameters and operations such as locomotive cooling, energy storage for hybrid vehicles, and friction handling materials can be used. The amount of cooling required can be adjusted based on anticipated demand. For example, if there is a high demand for forward tensile stress due to high grade, traction motors can be cooled directly in time to increase their short-term (technical) speed that will be required to produce superior tensile stresses. Similarly if there is a forward tunnel if the engine and other components can be pre-cooled to allow operation through a tunnel to be improved. Conversely, if there is a low forward demand, then cooling must be closed (or reduced) to take advantage of the thermal mass present in motor cooling and electrical equipment such as alternators, traction motors, electronic components, etc. Energy. In a hybrid vehicle, the amount of energy in a Hybrid Vehicle that must be transferred into and out of the energy storage system can be optimized based on the demand that will be required in the future. For example, if there is a large period of dynamic brake forward region, then all the energy in the storage system can now be consumed (instead of the motor) to have no stored energy from the start of the dynamic brake region ( so that the maximum energy can be captured again during the dynamic brake operating region). Similarly, if there is a heavy energy demand expected in the future, the stored energy can be increased for forward use. The amount and duration of distribution of friction increase material (sand) can be reduced if the equipment speed is not needed later. The tow axle energy / traction effort velocity can be increased to obtain maximum available adhesion without expanding these friction enhancement resources. There are other considerations for optimization other than fuel. For example, emissions may be another consideration especially in highly regulated cities or regions. In these regions it is possible to reduce emissions (smoke, nitrogen oxide, etc.) and discard other parameters such as fuel efficiency. Audible noise can be another consideration. Consumable conservation under certain obligations is another consideration. For example, distribution of sand or other friction modifiers in certain locations can be decreased. These location-specific optimization considerations can be based on current location information (obtained from operator inputs, trail entries, GPS / trail information along with ground guidance information). All these factors are considered both for current demand and for optimizations for the total operating plan.

HYBRID LOCOMOTIVE Referring to Figure 21, a hybrid locomotive level 2100 is shown having a power storage subsystem 2116. The energy management subsystem 2112 controls the energy storage subsystem 2116 and the various locomotive components, such as engine of diesel 2102, alternator 2104, rectifier 2106, mechanically known auxiliary loads 2108, and electric auxiliary loads 2110 that generate and / or use electric power. This 2112 operating subsystem operates to direct available electrical power such as that generated by the traction motors during dynamic braking or excess energy of the motor and alternator, to the energy storage subsystem 2116, and to release this stored electrical energy within the consistency to assist in the propulsion of the locomotive during monitoring operations. To do that, the energy management subsystem 2112 communicates with the diesel engine 2102, alternator 2104, converters and controllers 2120 and 2140 for the traction motors 2122 and 2142 and the energy storage subsystem interface 2126. As described above, A hybrid locomotive provides additional capabilities to optimize locomotive level 500 performance (and thus consistency and train level). In some aspects, it allows the current engine performance to be decoupled from the current locomotive energy demands for monitoring, to allow engine operation to be optimized not only for current operating conditions, but also before the arrival of the engines. topography and operational requirements. As shown in Figure 21, the locomotive 2114 data, such as anticipated demand, anticipated energy storage opportunities, speed and location, are entered into the 2112 energy management subsystem of the locomotive design. The energy management subsystem 2112 receives data from and provides instructions to the controls and diesel engine system 2102, and the control and alternator and rectifier systems 2104 and 2106, respectively. The energy management subsystem 2112 provides control to the energy storage system 2128, the converters and controllers of the traction motors 2120 and 2140, and the braking grid resistances 2124. When the elements of the present invention or the modality (is) therefore, the articles "a", "one", "the", and "said" are meant to mean that there is one or more of the elements. The term "comprising", "including", and "having" are intended to be inclusive and mean that there may be additional elements other than the elements listed. Those skilled in the art will note that the order of execution or performance of the methods illustrated and described herein is not essential, unless otherwise specified. That means, it is contemplated that the aspects or steps of the methods can be performed in any order, unless otherwise specified, and that the methods may include more or less aspects or steps than those written here. While various embodiments of the present invention are illustrated and described, it will be appreciated by those skilled in the art that many changes and modifications may be made thereto without departing from the spirit and scope of the invention. While various changes may be made to the above interpretations without departing from the scope of the invention, it is intended that the entire subject matter contained in the foregoing description or shown in the accompanying drawings be construed as illustrative and not in a limiting sense.

Claims (13)

1. CLAIMS 1.- A multi-level system for managing a railway system (50) and its operational components, the railroad system (50) comprises: a first processor (202) associated with a level of railroad infrastructure ( 100) configured to control an operation of a railway track infrastructure operating within the railroad infrastructure level (100), a second processor (502) associated with a railroad track trace network level (200) configured to control an operation of a trail network of railroad tracks within the level of trail network of railroad tracks (200), said level of infrastructure of railway tracks (100) contains one or more levels of the trail network railroad tracks (200); a third processor (1002) associated with a train level (300) configured to control an operation of a train operating within the train level (300), said level of the railroad track network (200) contains one or more train levels (300); a fourth processor (1202) associated with a consistency level (400) configured to control an operation of a consistency of a train within the consistency level (400), said train level containing one or more consistency levels (400); and a fifth processor (1502) associated with a locomotive level (500) configured to control an operation of a locomotive within locomotive level (500), said level of consistency (400) contains one or more levels of locomotive (500); each processor (202, 502, 1002, 1202, and 1502) associated with each level (100, 200, 300, 400, 500) being configured to provide the associated processor with at least other operational level parameters that define operational characteristics and related data with the level at which they are associated, and each processor (202, 502, 1002, 1202, and 1502) optimizes the operation within its associated level (100, 200, 300, 400, 500) and to cooperate with processors associated with at least one other level for optimizing an operation of the railway system (50) through the levels (100, 200, 300, 400, 500) of the railway system (50) based on an optimization parameter.
2. 2. The system according to claim 1, wherein the first processor (202) associated with the railroad infrastructure level (100) receives one or more of: railroad infrastructure data (206); rail track trail network data (208); and train data (210); and controls an operation of a railroad infrastructure within the railroad infrastructure level (100) based at least in part on them; the second processor (502) associated with a trail network level of rail tracks (200) receives one or more of: railroad infrastructure data (506); network data of track of railroad tracks (508); and train data (510); and controls an operation of a railroad track network within a railroad track trace level (200) based at least in part thereon; the third processor (1002) associated with a train level (300) receives one or more of: railroad infrastructure data (1006); network data of track of railroad tracks (1008); train data (1010); and consistency data (1012); and controls an operation of a train within train level (300) based at least in part on it; the fourth processor (1202) associated with a consistency level (400) receives one or more of: train data (1210); consistency data (1212); and locomotive data (1214); and controls an operation of a consistency within a consistency level (400) based at least in part on them; the fifth processor (1502) associated with a locomotive level (500) receives one or more of: consistency level data (1512); and locomotive data (1514); and controls an operation of a locomotive within the locomotive level (500) based at least in part on it.
3. 3. - A multi-level system for the administration of a railway system (50) and its operational components, the rail system (50) comprises: a first level configured to optimize an operation within the first level, said first level includes operational parameters first level that define characteristics and operational data of the first level; and a second level configured to optimize an operation within the second level, said second level includes second level operational parameters that define the characteristic and operational data of the second level; said first level that provides the first level operational parameters to the second level, and the second level provides the second level with the operational parameters of the second level; and said optimization of the operation within the first level and said optimization of operation within the second level each being a function to optimize a system optimization parameter.
4. 4. The system according to claim 3, wherein the system optimization parameter is indicative of one or more of: fuel use; an economic valuation of the delivery time of the cargo transported in the railway system; predetermined changes in conditions; a speed of change in conditions; and a rate of change in one condition with respect to another.
5. 5. - The system according to claim 3, wherein optimizing the operation within the first level and optimizing the operation within the second level includes identifying key operative obligations and data in one of the first and second level and communicating these obligations and data to others of the first and second level to optimize performance at the other level.
6. 6. A method to optimize an operation of the railway system (50), said rail system has a first level and a second level, the method comprises: communicating from the first level to the second level a first level operational parameter that defines a operational characteristic of the first level; communicating from the second level to the first level a second operational parameter defining an operational characteristic of the second level optimizing a system operation through a combination of the first level and the second level based on a system optimization parameter; optimizing an operation within the first level based on a first level optimization parameter and based in part on the system optimization parameter; and optimizing an operation within the second level based on a second level optimization parameter and based in part on the system optimization parameter.
7. 7. The method according to claim 6, wherein the first level optimization parameter, the second level optimization parameter and the system optimization parameter are a common optimization parameter.
8. 8. The method according to claim 6, wherein the common optimization parameter is indicative of one or more of: fuel use; an economic valuation of the delivery time of the cargo transported in the railway system; predetermined changes in conditions; a speed of change in conditions; and a rate of change in one condition with respect to another.
9. 9. - The method according to claim 6, wherein the operational parameters are provided from one level to the other at predetermined intervals.
10. 10. The method according to claim 6, wherein the step of optimizing a system operation through a combination of the first level and the second level based on a system optimization parameter includes identifying key operational obligations and data in one. of the first and second levels and communicate these obligations and data to another of the first and second level to optimize the performance at the other level.
11. 11.- A multi-level system to manage the railway system and its operational components, the rail system includes: a first level that includes first-level operational parameters that define operational characteristics and first-level data; and a second level that includes second level operational parameters configured to optimize an operation within the second level and where the second level operational parameters are indicative of changes in characteristics and operational data of the second level; and said second level provides second level operaclonal parameters to the first level.
12. 12. The system according to claim 11, wherein said optimization of operation within the second level is a function for optimizing a railway system optimization parameter.
13. 13. The system according to claim 12, wherein the system optimization parameter is indicative of one or more of: a change in fuel use; a change in an economic assessment of the delivery time of cargo transported in the railway system; a speed of change in second-level operational parameters; a speed of change with respect to time; and a rate of change in one condition with respect to another.